Variational autoencoders to use latent factors in causal inference.
wilcas / vaecit Goto Github PK
View Code? Open in Web Editor NEWVariational autoencoders to use latent factors in causal inference
License: Apache License 2.0
Variational autoencoders to use latent factors in causal inference
License: Apache License 2.0
the goal is to run code to train an encoder quickly, solely to get a replicable latent representation of input genotypes. The result would ideally be:
Found a bug in simulating independent association, and in generating t statistics. As such I need to re-run the analyses I did on graham, and review those results.
This may not be as straightforward as a one-to-one comparison, but I think it'll still be a useful exercise at first to compare effect p values of CIT results from the xQTL paper to what I get grouping by gene and considering each eQTL SNP at once (even though they may not all directly associate with each probe). I will need to:
rosmap_cit_replication.py
running on orenthalCIT.txt
file on xQTLservewrite code for pairwise correlation between the two and examine the results. Could be a quick way to pick apart when one method is doing something "better" than the other
First ideas:
Based on Blessings of Multiple Causes paper, it may be appropriate to try som LFA type method fit to the criteria to account for confounds affecting multiple causes. I.e. take LVs from other LFA type methods, fit them to satsify some riteria about how they associate with what I'm summarizing with them, and then use those in causal inference.
if you see the simulations I have thus far, it seems that independent association is getting called as full mediation when it definitely shouldn't be.
At first it seemed like the reason may have been that I was using more genotypes, but the issue could be:
Need a larger test and need to get a distribution of p values that work
So far there are test cases written for code relating to sample matching and most data processing. I still need to put some code for:
-[x] existing discovered bugs and test clean up
-[ ] testing loading in data on orenthal
-[x] subsetting data
-[x] some more high-level data processing tasks (although these may be removed later).
In selection of other probes, there remains the issue of how to deal with data in expanding the number of mediation events detected. Ideally, I'd be using one nice LV method to reduce data down to 1 dimension, but it seems from simulation that this fails to capture the associations between that data and a target (i.e. more LVs are necessary).
I have a few suggestions as to how to continue/what to do to try to deal with this:
In each of the above cases I need to decide:
For now I'm going to group probes/peaks based on the gene they associate with within a window. Then I am going to take these top (bonferroni corrected threshold) QTMs/eQTAs and summarize them by LV.
To do this I will need to:
I still haven't decided on when, where, and if to explicitly account for hidden confounds in the data. In my design plan / code I should allocate some space for this.
MMD-VAE is taking a really long time on a simulation with 5x samples, but 1/4 genotypes. Some issue is probably there, may explore re-writing/writing in a different framework like PyTorch that's easier for me to understand.
I need to write, run, and test code for my eQTM and eQTA analysis required for testing my method genome-wide.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.